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This paper is concerned with model reduction for a complex Markov chain using state aggregation. The work is motivated in part by the need for reduced order estimation of occupancy in a building during evacuation. We propose and compare two distinct model reduction techniques, each of which is based on the potential matrix for the Markov semigroup. The first method is based on spectral graph partitioning...
This paper considers simulation and estimation with cellular automata based stochastic models of traffic of agents on a graph. For the purposes of Bayesian estimation, an inhomogeneous hidden Markov model is abstracted from the cellular automata model. The uncertainty-based metric of relative entropy is proposed to assess performance with the estimation. This metric is used to compare the actual distribution...
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